Companies Are Reimagining Business Processes with Algorithms

In the early 1990s, executives and managers welcomed information technology — databases, PC workstations, and automated systems — into their offices. They saw the potential for significant business gains. Computers wouldn’t just speed up processes or automate certain tasks — they could upset nearly all business processes and allow executives to rethink operations from the ground up. And so the reengineering movement was born.

Now it’s happening again. Powerful machine-learning algorithms that adapt through experience and evolve in intelligence with exposure to data are driving changes in businesses that would have been impossible to imagine just five years ago. The PCs and databases introduced during the reengineering of the 90s have grown up: the rules-based codes written by engineers are giving way to learning algorithms driven by the machines themselves. As a result, business processes are being machine-reengineered.

Algorithms aim to redesign business processes just like humans did during the original reengineering movement. Then, reengineering was limited by the speed of humans. Managers noted historical trends and revised processes, and engineers developed code that was then baked into computing systems. Every update or response to the market required multiple steps; it cost time and performance. Sometimes, by the time changes were in place, the market had already moved. With machine-reengineering, process changes are constant and driven not just by history but also by the predictive capabilities of machine-learning algorithms. Machine-reengineering asks that people train and actively manage the performance of the algorithms and data models that drive process change, rather than drive process change themselves.

Reengineering got off track by encouraging businesses to overhaul too many processes too quickly. Moreover, the reengineering rhetoric of “obliterate” was extreme and ultimately destructive not only to processes, but to businesses as well. Machine-reengineering seems to have so far avoided these mistakes. Businesses that machine-reengineer their processes focus on one core process at a time, and thus they can quantify positive outcomes.

In our study of more than 30 pilots in early-adopter companies, we found five common business processes improved by machine-reengineering. The chart shows the proportion of companies targeting a business process improvement category, and then the proportion of those processes that used various machine-learning techniques to accomplish the goal. For example, nearly half of the companies are using machine reengineering for marketing products and services. If you hover over that purple bar, you see they’re doing this predominantly through predictive analytics, but also mixing in some visual sensing and natural language detection.

Once we mapped the processes targeted to the machine-learning techniques used, we wanted to understand how those techniques connected to three desired outcomes for the business: improving cost performance, customer performance, and revenue performance. For example, hovering over natural language processing’s orange bar shows that it’s creating improvements in cost and customer performance in near equal measure, but it’s not really applied to revenue performance.

That’s the proportional view of activities. How is all this machine reengineering actually paying off? Though this is just the beginning (we suspect many more processes will follow) we already see evidence of significant, even exponential, business gains in these three areas.

Nearly half of early movers reported improvements to top-line performance. Most often, improvements came through automatically providing more timely predictive data to employees who interact with customers or sales prospects.

A San Francisco-based business services company noted shortcomings in the traditional reengineered approach to its sales and marketing process, in which Customer Relationship Management (CRM) databases were scoured for potential leads using relatively static algorithms. Algorithms couldn’t deal well with data decay, quality-assurance issues, and long turnaround times. But after machine-reengineering this process, the firm has access to up-to-date buyer behavior that lets them predict market segments with the biggest potential for growth.

The company calls the new process a “scientific revenue machine” or SRM. So far, it has helped to increase revenue 20 fold and unearthed market segments 2.5 times more likely to convert. Moreover, this machine-engineering has freed up the company’s data analysts. They’re now redirecting their attention toward developing new products, further enhancing revenue capabilities.

More than a third of early movers also saw gains in bottom-line performance using machine-reengineering to slash 15% to 70% of costs from certain processes. At the same time, some saw a tenfold improvement in workforce effectiveness or value creation.

In one dramatic case, a global consumer food company machine-reengineered the delivery of its products in a striking new way — significantly reducing costly accidents and delays. Previously, the standard approach to its risk management process included monitoring business assets and conducting root-cause analyses on truck accidents after the fact.

With machine-reengineering, the company has implemented Mobileye Collision Avoidance Systems, which uses an “intelligent vision sensor.” The systems scan the road while applying computer-vision algorithms. They continuously measure the distance of potential obstacles and speed of other vehicles and alert drivers to imminent dangers, improving reaction times. A pilot program reduced accidents due to insufficient headway by 40%. Forward collisions were reduced by 50%. And lane departure incidents were cut by 80%. Predictive powers gained by machine-reengineering are fundamentally improving the safety of operations.

About a fifth of early movers reported significant gains in customer satisfaction and engagement. Here, we can thank machine-reengineered processes for smoothing customer-service interactions, reducing process steps, or increasing human interaction in customer service situations.

For years, reengineering drove companies to move more and more customer service toward automation. Unfortunately, customers never warmed to audio menu options, computerized voices, and lengthy authentication processes. Machine-reengineered systems can improve these interactions.

Nuance FreeSpeech is a system that verifies a caller’s identity through the course of natural conversation, offering alternatives to caller identification, eliminating the cumbersome and seemingly redundant series of questions often used to confirm identity.

A Canada-based financial services group uses active biometrics called VocalPassword in both French and English. By using customers’ voices as passwords, up to four steps in the authentication process have now been eliminated. The company reports a 50% improvement in call routing.

By comparison, a European bank has deployed a passive form of voice biometrics used with high-net worth clients to speak to their financial advisor — the system simply listens and matches voice signatures as a conversation naturally progresses. Average call handling time has been reduced by 15 seconds, and customers are pleased: 93% of clients rate the system 9 out of 10.

Another organization, this one based in Australia, receives roughly nine million calls per year with 75% requiring authentication. It has introduced both passive and active voice biometrics so that conversations don’t begin with a long set of questions. The average call length has been cut by at least 40 seconds.

Machine-reengineering not only creates new workflows, but a wholly new model for thinking about work and processes. It has the potential to augment our thinking beyond cause and effect and allow us to understand, and then improve operations that are too complex for the human mind to manage, in some ways making the previously invisible visible. It will make processes far more agile, efficient and productive. If the early adopters are any indication, machine-engineering is a leap forward in the evolution of business processes. The rewards are there, waiting to be found.